MEAN SQUARE EXPONENTIAL STABILITY OF GENERALIZED STOCHASTIC NEURAL NETWORKS WITH TIME-VARYING DELAYS

被引：8

作者：

Yu, Jianjiang ^{[1
,2
]}

Zhang, Kanjian ^{[1
]}

Fei, Shumin ^{[1
]}

机构：

[1] Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China

[2] Yancheng Teachers Univ, Sch Informat Sci & Technol, Yancheng 224002, Peoples R China

来源：

ASIAN JOURNAL OF CONTROL | 2009年 / 11卷 / 06期

关键词：

Neural networks; stochastic system; delay-dependent criteria; mean square exponential stability; GLOBAL ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; DEPENDENT STABILITY; DISCRETE; PERIODICITY; CRITERIA;

D O I：

10.1002/asjc.144

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the mean square exponential stability problem is dealt with a class of uncertain generalized stochastic neural networks with time-varying delays. By introducing a new Lyapunov-Krasovskii functional, improved delay-dependent stability criteria are established in terms of linear matrix inequalities. The activation functions are assumed to be of more general descriptions, which generalize and improve those earlier methods. Finally, a numerical example is given to show that our results are less conservative and more efficient than the existing stability criteria.

引用

页码：633 / 642

页数：10

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